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2024人工智能治理:隐私专家的进阶课程.pdf

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1、Generative AI Governance 101A Masterclass for Privacy ProsTina HwangChief Privacy Officer and VP of LegalAncestryDaniel M.GoldbergChair&Partner,Privacy and Data Security GroupFrankfurt Kurnit Klein&SelzBen BrookCEO&Co-founderTranscendAlan Wilemon DirectorINQ ConsultingMeet the panelI.AIs current leg

2、al landscapeII.Defining the governance problemIII.Addressing AI risksIV.Building an effective AI governance programV.Technical AI governanceAgendaI.Open the camera app on your phoneII.Scan the QR code on the slideIII.Make your selectionIV.Press Send(dont forget!)V.View the rooms results on your phon

3、eHow to vote in pollsPoll:How would you describe your role in relation to AI governance?I.De-facto Chief AI OfficerII.Robin to another teams BatmanIII.Learning and cheering from the sidelinesIV.Wait,whats AI governance?AIs current legal landscapeI.EU AI ActA.Risk-based approach:Unacceptable,high,lim

4、ited,minimalB.Gen AI tools are not high-risk,but must follow transparency and copyright rulesC.Expected to come online in May or June of 2024II.CPRA rulemakingA.Provide pre-notice about the use of automated decision-making technology(ADT),a consumer opt-out mechanism,and disclosures about how ADT is

5、 usedB.Concerns over broad definition of ADT and exceptions for opt-out rightsC.Sent back to the CPRA rules committeeIII.FTC scrutiny and enforcementA.“Keep your AI claims in check”B.Avoid exaggeration/false promises and proactively manage riskPoll:What is the greatest AI risk organizations face?I.R

6、eputational damageII.Privacy infractionsIII.Intellectual property issuesIV.TransparencyV.BiasVI.OtherDefine the problem to address AI risks strategically I.Predictive AI vs.generative AIII.Building AI vs.getting on the bandwagonIII.Different levels of risk depending on your use caseAddressing AI pri

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本文主要介绍了生成式人工智能治理的入门级课程,重点关注隐私专业人士。内容包括:AI的法律现状、定义治理问题、解决AI风险、建立有效的AI治理程序、技术AI治理等。 关键数据: 1. AI的法律现状:欧盟AI法案预计于2024年5月或6月上线,加州消费者隐私法规(CPRA)正在制定中,美国联邦贸易委员会(FTC)对AI声明进行严格审查。 2. AI治理问题:AI的预测性与生成性、自主风险、信息风险、偏见风险等。 3. AI隐私风险:个体偏见、群体刻板印象、信息泄露、预测性信息泄露、行为操纵等。 4. AI治理程序:理解公司如何使用AI、将治理目标与商业目标相结合、制定AI/ML原则、跨部门合作、执行政策与流程、定期审计与监控等。 关键点: 1. AI的法律现状与挑战。 2. AI治理问题的定义与风险。 3. 建立有效的AI治理程序,包括技术治理措施。 4. 针对AI风险的应对策略。 5. AI治理的责任归属与组织结构。
如何定义和应对风险?" 欧洲AI法案与加州CPRA规则解读" 建立有效治理程序与技术管理策略"
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